John Lagergren, Ph.D.
About John Lagergren, Ph.D.
John Lagergren, Ph.D., is a Postdoctoral Research Associate at Oak Ridge National Laboratory, where he has worked since 2021. His research focuses on developing scientific machine learning methods for biological data and involves collaboration on multi-institutional projects related to human, plant, and microbial systems.
Current Role at Oak Ridge National Laboratory
John Lagergren, Ph.D., serves as a Postdoctoral Research Associate at Oak Ridge National Laboratory. He has held this position since 2021, contributing to various research initiatives for three years. His work primarily involves collaboration on medium-to-large, multi-institutional projects that focus on biological processes related to human, plant, and microbial systems.
Educational Background
John Lagergren completed his Bachelor of Science in Computational and Applied Mathematics at East Tennessee State University from 2011 to 2015. He then pursued a Master of Science in Applied Mathematics at North Carolina State University, graduating in 2018. Following this, he continued at North Carolina State University, where he earned his Doctor of Philosophy in Applied Mathematics in 2020.
Previous Work Experience
Prior to his current role, John Lagergren worked in various positions that enhanced his research skills. He was a Lead Machine Learning Scientist at Off Camber Creative LLC from 2021 to 2022. He also served as a Graduate Research Assistant at North Carolina State University from 2016 to 2020. His earlier experiences include internships as a Machine Learning/AI Researcher at ARA in 2019 and 2020, and a Graduate Research Fellow at SAMSI from 2018 to 2019.
Research Focus and Expertise
John Lagergren specializes in developing scientific machine learning methods aimed at data-driven discovery and processing of biological data. His research interests include data-driven equation learning, theory-informed deep learning, computer vision, and geometric deep learning. He collaborates on projects that leverage these methodologies to advance understanding in biological systems.